Teaching

Graduate Courses

MASC 575: Basics of Atomistic Simulation of Materials
MASC 576: Molecular Dynamics Simulations of Materials and Processes

These course introduce modern computational materials modeling methodologies — from atomistic simulations using Molecular Dynamics (MD) and Monte Carlo (MC) methods, quantum mechanical simulations via Density Functional Theory (DFT), and foundation material models to artificial intelligence (AI)–driven approaches for science and engineering students. The landscape of computational materials is rapidly changing. DFT has become a standard tool to investigate a wide range of materials properties. Remarkable progresses by AI and machine learning (ML) have resulted in numerous exciting fields including generative AIs, universal Machine Learning Interatomic Potential (uMLIP), and foundation models that achieve structure-property predictions, and computational material synthesis, QM-level accuracy at a fraction of computational cost. Students will learn from learge-scale simulations using high-performance computing to DL-based AI systems for materials.

MASC 515: Basics of Machine Learning for Materials
MASC 520: Mathematical Methods for Deep Learning

Thsese courses introduce Machine Learning (ML) and Python programming essentials for data-driven science and engineering. While Artificial Intelligence (AI), Deep Learning (DL), Reinforcement Learning (RL), and Large Language Models (LLMs) have been rapidly transforming how we interact with technology in society, making it challenging for beginners to enter. Python has become the industry-standard for ML technologies, thus basic software engineering paradigms and literacy are necessary to move from theoretical knowledge to real-world application. The courses are designed for students with any engineering and science backgrounds. No prior experience and knowledge in ML or Python programming is required.

Undergraduate Courses

MASC 110L: Materials Science
AME 231: Mechanical Behavior of Materials
CHE 405: Applications of Probability and Statistics for Chemical Engineers